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Remote Sensing
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Remote and Proximal Sensing for Precision Agriculture and Viticulture II
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Remote and Proximal Sensing for Precision Agriculture and Viticulture II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 2333

Special Issue Editor

Dr. Frédéric Cointault

E-Mail Website
Guest Editor
L’Institut Agro Dijon, UMR Agroecology, 26 Bd Dr Petitjean, BP87999, 21079 Dijon, CEDEX, France
Interests: crop phenotyping; image acquisition and processing; viticulture; plant disease; UAV
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote and proximal sensing are the two most common techniques concerning the acquisition of information about an object or any phenomenon without physical contact with the object. Remote sensing is widely tied to the use of satellite, airborne or UAV platforms using multi- or hyperspectral imagery. In terms of proximal sensing, the sensor is close to the object and is installed on platforms ranging from handheld, fixed installations, or robotics and tractor-embedded sensors. The types of sensors range from simple RGB or grey-level cameras to multispectral and hyperspectral high-resolution imaging systems or even thermographic cameras.

Associated with plant growth conditions and phenotyping techniques, remote and proximal sensing are able to provide information on nutrient deficiency, biotic stress such as pests and diseases as well as abiotic stresses, allowing precision agriculture and viticulture practices.

For this Special Issue, we welcome the submission of papers on both fundamental and applied research relating on Remote and Proximal Sensing for Precision Agriculture and Viticulture, combining spectral, spatial and temporal information based on multi- and hyperspectral imagery with the capabilities of management-oriented crop simulation models. We also invite papers dedicated to new sensors that can be used in agriculture, aiming at better management of crops, and methods for better crop management and a more respectful treatment of the environment.

This is the second edition of this Special Issue series. For more information on the first edition, please see: https://www.mdpi.com/journal/remotesensing/special_issues/Proximal_Sensing_for_Agriculture

Dr. Frédéric Cointault
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • proximal sensing
  • precision agriculture and viticulture
  • image acquisition
  • image processing
  • multi- and hyperspectral data and sensors

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Related Special Issue

Published Papers (3 papers)

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Research

21 pages, 8280 KiB  
Article
Segmentation of Multitemporal PlanetScope Data to Improve the Land Parcel Identification System (LPIS)
by Marco Obialero and Piero Boccardo
Remote Sens. 2025, 17(12), 1962; https://doi.org/10.3390/rs17121962 - 6 Jun 2025
Viewed by 181
Abstract
The 1992 reform of the European Common Agricultural Policy (CAP) introduced the Land Parcel Identification System (LPIS), a geodatabase of land parcels used to monitor and regulate agricultural subsidies. Traditionally, the LPIS has relied on high-resolution aerial orthophotos; however, recent advancements in very-high-resolution [...] Read more.
The 1992 reform of the European Common Agricultural Policy (CAP) introduced the Land Parcel Identification System (LPIS), a geodatabase of land parcels used to monitor and regulate agricultural subsidies. Traditionally, the LPIS has relied on high-resolution aerial orthophotos; however, recent advancements in very-high-resolution (VHR) satellite imagery present new opportunities to enhance its effectiveness. This study explores the feasibility of utilizing PlanetScope, a commercial VHR optical satellite constellation, to map agricultural parcels within the LPIS. A test was conducted in Umbria, Italy, integrating existing datasets with a series of PlanetScope images from 2023. A segmentation workflow was designed, employing the Normalized difference Vegetation Index (NDVI) alongside the Edge segmentation method with varying sensitivity thresholds. An accuracy evaluation based on geometric metrics, comparing detected parcels with cadastral references, revealed that a 30% scale threshold yielded the most reliable results, achieving an accuracy rate of 83.3%. The results indicate that the short revisit time of PlanetScope compensates for its lower spatial resolution compared to traditional orthophotos, allowing accurate delineation of parcels. However, challenges remain in automating parcel matching and integrating alternative methods for accuracy assessment. Further research should focus on refining segmentation parameters and optimizing PlanetScope’s temporal and spectral resolution to strengthen LPIS performance, ultimately fostering more sustainable and data-driven agricultural management. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture II)
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18 pages, 1917 KiB  
Article
In-Season Potato Nitrogen Prediction Using Multispectral Drone Data and Machine Learning
by Ehsan Chatraei Azizabadi, Mohamed El-Shetehy, Xiaodong Cheng, Ali Youssef and Nasem Badreldin
Remote Sens. 2025, 17(11), 1860; https://doi.org/10.3390/rs17111860 - 27 May 2025
Viewed by 318
Abstract
Assessing nitrogen (N) status in potato (Solanum tuberosum L.) during the growing season is crucial for optimizing fertilizer application, aligning it with crop demand, and improving N use efficiency, particularly in Western Canada, where extensive potato cultivation supports the agricultural industry. This [...] Read more.
Assessing nitrogen (N) status in potato (Solanum tuberosum L.) during the growing season is crucial for optimizing fertilizer application, aligning it with crop demand, and improving N use efficiency, particularly in Western Canada, where extensive potato cultivation supports the agricultural industry. This study evaluated the performance of three machine learning (ML) models—Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Regression (GBR)—for predicting potato N status and examined the impact of feature selection techniques, including Partial Least Squares Regression (PLSR), Boruta, and Recursive Feature Elimination (RFE). A field experiment was conducted in 2023 and 2024 near Carberry, Manitoba, Canada, with plots receiving different N rates from various fertilizer sources. Multispectral drone imagery was collected throughout the growing seasons, and key vegetation indices (VIs) related to plant N concentration were extracted for model training. Among the VIs, Cl green exhibited the highest correlation with petiole NO3-N concentration (PNC). The results indicate that RF outperformed SVM and GBR, achieving the highest coefficient of determination (R2 = 0.571) and the lowest mean absolute error (MAE = 0.365%) using the RFE feature selection method. Feature selection enhanced model performance in specific cases, notably RF with RFE, and both SVM and GBR with Boruta. These findings highlight the potential of ML-based approaches for in-season potato N monitoring and emphasize the importance of feature selection in enhancing predictive accuracy. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture II)
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18 pages, 3655 KiB  
Article
Investigating the Role of Cover-Crop Spectra for Vineyard Monitoring from Airborne and Spaceborne Remote Sensing
by Michael Williams, Niall G. Burnside, Matthew Brolly and Chris B. Joyce
Remote Sens. 2024, 16(21), 3942; https://doi.org/10.3390/rs16213942 - 23 Oct 2024
Cited by 1 | Viewed by 1164
Abstract
The monitoring of grape quality parameters within viticulture using airborne remote sensing is an increasingly important aspect of precision viticulture. Airborne remote sensing allows high volumes of spatial consistent data to be collected with improved efficiency over ground-based surveys. Spectral data can be [...] Read more.
The monitoring of grape quality parameters within viticulture using airborne remote sensing is an increasingly important aspect of precision viticulture. Airborne remote sensing allows high volumes of spatial consistent data to be collected with improved efficiency over ground-based surveys. Spectral data can be used to understand the characteristics of vineyards, including the characteristics and health of the vines. Within viticultural remote sensing, the use of cover-crop spectra for monitoring is often overlooked due to the perceived noise it generates within imagery. However, within viticulture, the cover crop is a widely used and important management tool. This study uses multispectral data acquired by a high-resolution uncrewed aerial vehicle (UAV) and Sentinel-2 MSI to explore the benefit that cover-crop pixels could have for grape yield and quality monitoring. This study was undertaken across three growing seasons in the southeast of England, at a large commercial wine producer. The site was split into a number of vineyards, with sub-blocks for different vine varieties and rootstocks. Pre-harvest multispectral UAV imagery was collected across three vineyard parcels. UAV imagery was radiometrically corrected and stitched to create orthomosaics (red, green, and near-infrared) for each vineyard and survey date. Orthomosaics were segmented into pure cover-cropuav and pure vineuav pixels, removing the impact that mixed pixels could have upon analysis, with three vegetation indices (VIs) constructed from the segmented imagery. Sentinel-2 Level 2a bottom of atmosphere scenes were also acquired as close to UAV surveys as possible. In parallel, the yield and quality surveys were undertaken one to two weeks prior to harvest. Laboratory refractometry was performed to determine the grape total acid, total soluble solids, alpha amino acids, and berry weight. Extreme gradient boosting (XGBoost v2.1.1) was used to determine the ability of remote sensing data to predict the grape yield and quality parameters. Results suggested that pure cover-cropuav was a successful predictor of grape yield and quality parameters (range of R2 = 0.37–0.45), with model evaluation results comparable to pure vineuav and Sentinel-2 models. The analysis also showed that, whilst the structural similarity between the both UAV and Sentinel-2 data was high, the cover crop is the most influential spectral component within the Sentinel-2 data. This research presents novel evidence for the ability of cover-cropuav to predict grape yield and quality. Moreover, this finding then provides a mechanism which explains the success of the Sentinel-2 modelling of grape yield and quality. For growers and wine producers, creating grape yield and quality prediction models through moderate-resolution satellite imagery would be a significant innovation. Proving more cost-effective than UAV monitoring for large vineyards, such methodologies could also act to bring substantial cost savings to vineyard management. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture II)
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